This invention relates to new and useful improvements to the control of complex multi-variable continuous manufacturing processes. More specifically, the invention relates to the use of techniques to develop, implement and use a neural network to dynamically monitor and adjust a manufacturing process to attain a product that, for example, meets optimum quality and production requirements.
Process optimization contemplates a wide variety of situations where there is a need to control variables that are not directly or instantaneously controllable. In these situations, the only mechanism (PVs) for control is the manipulation of variables but not a way that can be easily determined mathematically. A human process expert can empirically derive an algorithm to optimize the indirectly controlled variables; however, as the number of PVs that influence the indirectly controlled variables increase, the complexity of the problem grows by the square of the increase. Since this condition quickly becomes unmanageable, the PVs with less influence are ignored in the solution. Although each of these PVs exhibit low influence when considered alone, the cumulative effect of multiple PVs can have significant influence. Their omission from the optimization algorithm can greatly reduce its accuracy and usability.
FIG. 1 illustrates the typical problem. The physical process block represents any complex multi-variable continuous process where several process variables are controlled to produce optimized outputs. The individual PVs are controlled by independent proportional-integral-derivative (PID) controllers in a conventional closed loop strategy. Process variables that cannot be controlled directly or immediately are labeled PV'1, PV'2.
The setpoint(s) (SP) of each of these PID loops is determined by the controller which may simply be a process expert (foreman, process engineer, etc.) to produce the desired outputs from the physical process for the current conditions. If the outputs deviate from the optimum, the process expert decides which setpoint(s) should be adjusted and the amount of the adjustments. In many processes, there may be a significant lag time between corrective action taken and the results of that action. This lag time is not caused by the response time of the individual PID loops, but is a result of the natural time constant of the physical process. PID loops normally respond rapidly when compared to the total process time constant.
There are problems with trying to optimize this type of process because of the process time constants and shifts in the steady state domain of the process. The corrections to these problems are not easily determined mathematically. It requires the experience of a process expert to derive the corrective actions.
Efforts to overcome and develop optimum solutions to the above problems have varied. In the simplest case multi-variable linear regression has been used to develope correlation equations. In more complex applications, actual mathematical models have been developed to approximate the process so that results are projected and changes to the SPs are made without having to wait for the process to respond with its normal time cycle. In the most recent efforts, the experiences of the process experts have been reduced to a set of rules that are implemented in a computer and/or microprocessor to change the SPs. A rule based expert system "quantifies" human intelligence, building a database of rules, derived from one or multiple experts, to govern what to do when predefined measurable situations arise.
This prior art has shortcomings: 1) known mathematical relationships between the process variables being considered may not exist; 2) assumptions must be made to reduce the relationships between the process variables to a domain that can be mathematically modeled; and 3) expert rules cannot be derived because experts do not exist or multiple experts do exist and their opinions differ.
Neural networks or neural nets are, as the name implies, networks of neurons (elements) that interconnect in a unique way. Typically, the networks comprise input neurons that receive signals or information from outside the network, output neurons that transmit signals or information outside the network and at least one hidden layer of neurons that receive and pass along information to other neurons. The potential advantage of neural nets is that, unlike classification logic, they can work with less than perfect input information. See "Neural Networks Part, 1", IEEE Expert, Winter 1987 and "Neural Networks Part, 2", IEEE Expert Spring 1988. The application of neural networks to commercial applications has not been reported. Two theoretical articles found by applicants relate to very simple or simulated use of neural networks for control. "An Associative Memory Based Learning Control Scheme With PI-Controller For SISO-Nonlinear Processes", Ersu and Wienand IFAC Microcomputer Application in Process Control (1986) and "A Symbolic-Neural Method for Solving Control Problems", Suddarth, Sutton and Holden, IEEE International Conference on Neural Networks, Vol. 1 (1988).
Recently, software for emulating neural networks on personal size computers has become commercially available. One such program sold under the Trademark "NeuroShell" is described in the accompanying user manual "NeuroShell Neural Network Shell Program", Ward Systems Group, Inc., Frederick, MD 21701 (1988). This manual is incorporated herein by reference.
It is an advantage, according to this invention, to use the existing neural network technology, either hardware or software, in a user configurable system for process optimization.